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To fill NaN with Linear Interpolation, use the interpolate() method on the Pandas series. At first, import the required libraries −import pandas as pd import numpy as npCreate a Pandas series with some NaN values. We have set the NaN using the numpy np.nan −d = pd.Series([10, 20, np.nan, 40, 50, np.nan, 70, np.nan, 90, 100]) Find linear interpolation −d.interpolate()ExampleFollowing is the code −import pandas as pd import numpy as np # pandas series d = pd.Series([10, 20, np.nan, 40, 50, np.nan, 70, np.nan, 90, 100]) print"Series...", d # interpolate print"Linear Interpolation...", d.interpolate()OutputThis will produce the following ... Read More
To make a frequency histogram from a list with tuple elements in Python, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Make a list of tuples, data.Make lists of frequency and indices, after iterating the data.Make a bar plot usig bar() method.To display the figure, use show() method.Exampleimport matplotlib.pyplot as plt plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True data = [("a", 1), ("c", 3), ("d", 4), ("b", 2), ("e", 7), ("f", 3), ('g', 2)] ind = [] fre = [] for item in data: ... Read More
To group dataframe rows into list, use the apply() function. At first, let us import the require library −import pandas as pdCreate DataFrame with 2 columns −dataFrame = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } )Grouping DataFrame into list with apply(list) −dataFrame = dataFrame.groupby('Car')['Units'].apply(list) ExampleFollowing is the code −import pandas as pd # Create DataFrame dataFrame = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": ... Read More
To rotate a simple matplotlib axes, we can take the following steps −Import the required packages −import matplotlib.pyplot as plt from matplotlib.transforms import Affine2D import mpl_toolkits.axisartist.floating_axes as floating_axesSet the figure size and adjust the padding between and around the subplots.Create a new figure or activate an existing figure.Make a tuple of axes extremes.Add a mutable 2D affine transformation, "t". Add a rotation (in degrees) to this transform in place.Add a transform from the source (curved) coordinate to target (rectilinear) coordinate.Add a floating axes "h" with the current figure with GridHelperCurveLinear() instance.Add an 'ax' to the figure as part of a ... Read More
To merge Pandas DataFrame, use the merge() function. The right outer join is implemented on both the DataFrames by setting under the “how” parameter of the merge() function i.e. −how = “right”At first, let us import the pandas library with an alias −import pandas as pd Create two dataframes to be merged −# Create DataFrame1 dataFrame1 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Audi', 'Mustang', 'Bentley', 'Jaguar'], "Units": [100, 150, 110, 80, 110, 90] } ) # Create DataFrame2 dataFrame2 = pd.DataFrame( { "Car": ['BMW', 'Lexus', 'Tesla', 'Mustang', 'Mercedes', 'Jaguar'], "Reg_Price": ... Read More
To add a 3D subplot to a matplotlib figure, we can take the following steps −Set the figure size and adjust the padding between and around the subplots.Create x, y and z data points using numpy.Create a new figure or activate an existing figure.Add an 'ax' to the figure as part of a subplot arrangement with projection='3d'.Plot x, y and z data points using plot() method.To display the figure, use .show() method.Examplefrom matplotlib import pyplot as plt import numpy as np # Set the figure size plt.rcParams["figure.figsize"] = [7.00, 3.50] plt.rcParams["figure.autolayout"] = True # Create x, y and ... Read More
When it is required to group strings by K length using a suffix, a simple iteration and the ‘try’ and ‘except’ blocks are used.ExampleBelow is a demonstration of the samemy_list = ['peek', "leak", 'creek', "weak", "good", 'week', "wood", "sneek"] print("The list is :") print(my_list) K = 3 print("The value of K is ") print(K) my_result = {} for element in my_list: suff = element[-K : ] try: my_result[suff].append(element) except: my_result[suff] = [element] print("The ... Read More
When it is required to replace the elements of the list by greatest neighbours, a simple iteration along with the ‘if’ and ‘else’ condition is used.ExampleBelow is a demonstration of the samemy_list = [41, 25, 24, 45, 86, 37, 18, 99] print("The list is :") print(my_list) for index in range(1, len(my_list) - 1): my_list[index] = my_list[index - 1] if my_list[index - 1] > my_list[index + 1] else my_list[index + 1] print("The resultant list is :") print(my_list)OutputThe list is : [41, 25, 24, 45, 86, 37, 18, 99] The resultant list is : [41, ... Read More
To filter DataFrame between two dates, use the dataframe.loc. At first, import the required library −import pandas as pdCreate a Dictionary of lists with date records −d = {'Car': ['BMW', 'Lexus', 'Audi', 'Mercedes', 'Jaguar', 'Bentley'], 'Date_of_Purchase': ['2021-07-10', '2021-08-12', '2021-06-17', '2021-03-16', '2021-02-19', '2021-08-22'] }Creating dataframe from the above dictionary of listsdataFrame = pd.DataFrame(d) Fetch car purchased between two dates i.e. 1st Date: 2021-05-10 and 2nd Date: 2021-08-25 −resDF = dataFrame.loc[(dataFrame["Date_of_Purchase"] >= "2021-05-10") & (dataFrame["Date_of_Purchase"] = "2021-05-10") & (dataFrame["Date_of_Purchase"] Read More
When it is required to filter dictionaries with ordered values, the ‘sorted’ method along with the list comprehension is used.ExampleBelow is a demonstration of the samemy_list = [{'python': 2, 'is': 8, 'fun': 10}, {'python': 1, 'for': 10, 'coding': 9}, {'cool': 3, 'python': 4}] print("The list is :") print(my_list) my_result = [index for index in my_list if sorted( list(index.values())) == list(index.values())] print("The resultant dictionary is :") print(my_result)OutputThe list is : [{'python': 2, 'fun': 10, 'is': 8}, {'python': 1, 'coding': 9, 'for': 10}, {'python': 4, 'cool': 3}] The resultant dictionary ... Read More